The present invention relates to digital wireless communication apparatus, and particularly relates to superior technology for suppressing click noise while maintaining call distance even when code errors occur in ADPCM code and alleviating deterioration in communication quality.
ADPCM (adaptive differential PCM) methods are often used as audio encoding methods for digital cordless telephones. ADPCM encoding methods have the property where click noise that is unexpectedly abrupt to the ear is generated when code errors occur when the influence of weak electric fields, phasing, and electromagnetic interference etc. is incurred so as to cause coding errors in audio data, thus causing audio quality to substantially deteriorate. In order to suppress this click noise, methods subjecting frame data where code errors have been detected by frame error checks such as Cyclic Redundancy Checks to muting processing are typical. However, in cases where there is one main unit acting as a base station as with a digital cordless telephone, there is the problem that the call distance is substantially limited. Further, this causes a voice to be suddenly muted during a call, which causes discomfort for the caller.
In order to resolve this problem, the applicant proposed digital wireless communication apparatus 300 shown in FIG. 8 (Japanese Patent Laid-open Publication No. 2006-50476). Digital wireless communication apparatus 300 is equipped with an ADPCM decoder 100, determination time adjustment section 200, code substituter 210, and error detector 220. The ADPCM decoder 100 is equipped with an adaptive de-quantizer 110, adaptive predictor 120, prediction signal limiter 130, regenerative signal calculator 140, output limiter 150, delay unit 160, quantization scale factor adapter 170, adaptive speed controller 180, and tone and changing point detector 190.
When error information is detected at the error detector 220, the determination time adjustment section 200 outputs an error detection signal indicating a frame period where code substitution processing may be validly executed to the code substituter 210. The code substituter 210 sequentially monitors a high-speed scale factor yu(k) and a low-speed scale factor yl(k) managed within the quantization scale factor adapter 170 every one sampling for data sections outputting error detection signals, and in the event that yl(k−1) for one sample previous exceeds one of a plurality of threshold values and y(k−1) of one sample previous exceeds a threshold value corresponding to l(k) and yl(k) at this time, it is predicted that click noise will occur, and l(k) is substituted with predetermined code l′(k).
The adaptive de-quantizer 110 then generates a quantization differential signal dq(k) based on ADPCM code l(k) (or l′(k)) and quantization scale factor y(k), and outputs the quantization differential signal dq(k) to the adaptive predictor 120, regenerative signal calculator 140, and tone and changing point detector 190.
The prediction signal limiter 130 compares a prediction signal se(k) and the value of a PCM output so(k−1) for one sample previous. In the event that the input signal is lower than a certain frequency so that so(k−1) is a maximum and se(k) is inverted code for so(k−1), or in the event that the input signal is higher than a certain frequency so that so(k−1) is a maximum and se(k) is a maximum of inverted code of so(k−1), it is predicted that this will generate click noise, se(k) is substituted with the same value as for so(k−1), and these are outputted as se′(k). The prediction signal limiter 130 outputs prediction signal se(k) as is to the regenerative signal calculator 140 when it is not necessary to carry out limiting processing.
The regenerative signal calculator 140 generates a regenerative signal sr(k) based on the quantization differential signal dq(k) and prediction signal se(k) (or se′(k)). The output limiter 14 compresses a regenerative signal sr(k) to a PCM signal so(k). Here, “k” is a variable indicating sampling time.
Further, detection of the input frequency is carried out by determining whether or not a convergent value of a1(k) exceeds a predetermined threshold value utilizing a frequency following characteristic of polar prediction function a1(k) shown in FIG. 9.